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Abstract

We assessed whether home blood pressure monitoring improved the prediction of progression of albuminuria when added to office measurements and compared it with ambulatory blood pressure monitoring in a multiethnic cohort of older people (n=392) with diabetes mellitus, without macroalbuminuria, participating in the telemedicine arm of the Informatics for Diabetes Education and Telemedicine Study. Albuminuria was assessed by measuring the spot urine albumin:creatinine ratio at baseline and annually for 3 years. The ambulatory sleep:wake systolic blood pressure ratio was categorized as dipping (ratio: ≤0.9), nondipping (ratio: >0.9 to 1.0), and nocturnal rise (ratio: >1.0). In a repeated-measures mixed linear model, after adjustment that included office pulse pressure, home pulse pressure was independently associated with a higher follow-up albumin:creatinine ratio (P=0.001). That association persisted (P=0.01) after adjusting for 24-hour pulse pressure and nocturnal rise, which were also independent predictors (P=0.02 and P=0.03, respectively). Cox proportional hazards models examined the progression of albuminuria (n=74) as defined by cutoff values used by clinicians. After the adjustment for office pulse pressure, the hazards ratio (95% CI) per 10-mm Hg increment of home pulse pressure was 1.34 (range: 1.1 to 1.7; P=0.01). Home pulse pressure was not an independent predictor in the model including ambulatory monitoring data; a nocturnal rise was the only independent predictor (P=0.035). Cox models built separately for home pulse pressure and ambulatory monitoring exhibited similar calibration and discrimination. In conclusion, nocturnal blood pressure elevation was the strongest predictor of worsening albuminuria. Home blood pressure measurements added to office measurements and may constitute an adequate substitute for ambulatory monitoring.

Albuminuria is independently associated with cardiovascular morbidity and mortality in people with and without diabetes mellitus.1–6 An increase in albuminuria is associated with higher cardiovascular morbidity and mortality,7 whereas a decrease achieved through drug therapy is associated with better outcomes.8 Albuminuria is prevalent in older and middle-aged people with type 2 diabetes mellitus,9–11 in whom cardiovascular and renal complication rates are the highest.12–14 Thus, it is of particular importance to identify predictors of worsening albuminuria in older people with diabetes mellitus.

Ambulatory blood pressure monitoring (ABPM) predicts progression of albuminuria better than office blood pressure (BP) in people with diabetes,15–19 and 24-hour pulse pressure (PP) and a nocturnal increase in BP are the most informative variables in elderly diabetic subjects.18,19 However, ABPM is not yet considered the standard of care for the management of hypertension. On the other hand, a growing number of patients are successfully monitoring their BP at home using oscillometric devices.20,21 In longitudinal studies, home monitoring outperformed office BP measurements in predicting cardiovascular events in hypertensive patients.22–24 As noted above, progression of albuminuria is independently associated with cardiovascular risk and may help identify patients at need for more aggressive clinical management. Therefore, it is important to determine whether home BP improves the prediction of worsening albuminuria in people with diabetes when added to office BP measurements and how it compares with ABPM in that regard. Although cross-sectional studies have shown that the association of prevalent albuminuria with home BP is stronger than with office BP25 and comparable to that with ABPM,26 to the best of our knowledge, there have been no longitudinal studies examining the association with worsening albuminuria. We, therefore, tested the hypothesis that home BP improves the prediction of worsening albuminuria in people with diabetes above and beyond office BP and compared the predictive information provided by home BP with that provided by ABPM. We carried out this study in a multiethnic cohort of people with diabetes, who performed self-monitoring of home BP as part of a telemedicine diabetes care intervention in a randomized, controlled trial and underwent ABPM at their baseline examination visit.

Methods

Study Participants

We studied participants enrolled in the intervention (telemedicine) arm of the multicenter Informatics for Diabetes Education and Telemedicine (IDEATel) Study, which has been described in detail elsewhere27 and which evaluates telemedicine as a means of managing the care of Medicare beneficiaries with diabetes.

This study analyzed relationships between BP data obtained at the baseline examination, and urinary albumin, measured by spot urine albumin:creatinine ratio (ACR), at 3 consecutive annual follow-up visits. Only intervention participants who completed ≥2 follow-up visits and provided urine albumin measurements were included (n=392). Details of the IDEATEL protocol are described in the online supplement (available at http://hyper.ahajournals.org).

Home BP Monitoring

Home BP was measured using an oscillometric device (UA-767, A&D Medical). Participants were fitted with the appropriate-sized cuff during the baseline visit and trained in the use of the BP monitor. Participants were encouraged to take their BP measurements several times a day (at least twice) at different times and while taking their usual antihypertensive medications. For these analyses, we used home BP measurements obtained within 60 days of the office and ambulatory BP measurements.

Office BP Measurement

Office BP was measured at the baseline visit using the Dinamap Pro 100 (Critikon) automated oscillometric device. Participants were instructed to take their antihypertensive medications as usual the morning of the examination. Three measurements were obtained at 1-minute intervals in a seated position after 5 minutes of rest in a quiet room, using a standardized protocol.28 The average of the second and third measurements was recorded as the resting BP. Office PP was defined as the difference between systolic and diastolic resting BP.

Ambulatory BP Monitoring

ABPM was performed at the baseline visit using a Spacelabs 90207 monitor (SpaceLabs) following a published protocol.29 BP was recorded every 20 minutes for a 24-hour period. Sleep and wake intervals were defined from diary entries and confirmed by a telephone interview on the morning when monitoring ended. A minimum of 6 valid wake readings and 4 valid sleep readings were required for the computation of wake and sleep averages. A reading was accepted as valid if it was nonartifactual and within physiological range. The mean (SD) number of measurements per participant was 64.5 (8.3), whereas the minimum number was 32 (in 1 participant). Ambulatory 24-hour PP was defined as the mean difference between all of the systolic and diastolic BP readings. Nocturnal dipping was defined as a ratio of mean sleep:mean wake systolic BP (SBP) of ≤0.90 (a decrease in sleep SBP of ≥10% relative to wake SBP). Nondipping was defined as a ratio of >0.9 to 1.0, and a nocturnal rise was defined as a ratio >1.0.30

ACR

ACR (milligrams of albumin per gram of creatinine) was calculated from a morning spot urine sample. Urine albumin excretion was categorized into normoalbuminuria (ACR <17 in men and <25 in women), microalbuminuria (ACR 17 to 250 in men and 25 to 355 in women), and macroalbuminuria (ACR >250 in men and >355 in women); these thresholds are designed to identify people with urinary albumin excretion rates >30 mg per 24 hours and >300 mg per 24 hours, respectively.31 Participants who had macroalbuminuria at baseline were excluded from this study. Progression of albuminuria was defined as a persistent increase in ACR to a higher category. In those with normoalbuminuria at baseline, progression was defined as microalbuminuria or macroalbuminuria at follow-up. In those with microalbuminuria at baseline, progression was defined as macroalbuminuria at follow-up. In participants with microalbuminuria at baseline, improvement in albuminuria was defined as having persistent measurements within a lower albuminuria category at follow-up visits. In all of the statistical analyses, this group was combined with those without progression. Thus, our analyses compared participants with progression of albuminuria with those without progression.

Statistical Analysis

Variables that were positively skewed, including ACR, were log10 transformed to better approximate a normal distribution. Comparisons of baseline characteristics according to category of progression of albuminuria were made using χ2 or Fisher’s exact tests (when any expected cell frequency was <5) for categorical variables, Student t for continuous variables approximating a normal distribution, and the Mann–Whitney U test for continuous variables that were not normally distributed. Correlations were assessed using Pearson’s or Spearman’s correlation, as appropriate. Receiver operating characteristic curves were used to identify the cutoff point for home PP that best predicted the progression of albuminuria.32,33

The goal of the multivariate analyses was to test the independent association of home BP with follow-up urine albumin excretion after adjustment for other covariates, including office and 24-hour BP. Two types of multivariate analyses were performed: repeated-measures mixed linear models, which considered ACR as a continuous variable, and Cox proportional hazards models, which considered albuminuria as a categorical dichotomous variable. PP measurements were selected as predictors for these models, because we previously found them to be the strongest predictors of progression of albuminuria in this population.18,34 Office and home PP were the BP variables entered in the first set of multivariate models, whereas ambulatory 24-hour PP, nocturnal BP patterns, and home PP were entered in the second model. All of the predictor variables were considered fixed at baseline (ie, none was treated as time dependent).

Repeated-measures mixed linear model analyses were performed with all of the available ACR measurements from the annual follow-up visits as the dependent variable, including baseline ACR and adjusting for clustering within physician panels in the study. The following covariates, assessed at baseline, were included in the mixed linear models because of their biologically plausible association with urine albumin excretion: age, gender, race, body mass index, use of angiotensin-converting enzyme inhibitors or angiotensin receptor blocker, number of antihypertensive medications, current smoking, duration of diabetes mellitus, hemoglobin A1c, serum triglycerides, and high-density lipoprotein cholesterol. Collinearity between BP variables was assessed by calculating the tolerance for each of them in the full models. None of them exhibited tolerance values <0.20, which would have indicated excessive collinearity.35

Given the relatively small number of events (n=74), the Cox models were adjusted for baseline ACR and those covariates that reached statistical significance in the mixed linear models as follows: race, body mass index, duration of diabetes mellitus, and current smoking. Correctness of the proportional hazards assumption was verified using the Harrell and Lee modification of the Schoenfeld goodness of fit test.36 First, we assessed the value of home PP when added to multivariate adjusted Cox models that included office BP on one hand and ABPM variables on the other. Secondly, we built separate Cox models for home BP and ABPM variables and compared those models in terms of goodness of fit (calibration) and discrimination.37 Calibration was measured using the −2 log-likelihood statistic and compared using the likelihood ratio test, whereas discrimination of each model was estimated calculating the c-statistic (area under the receiver operating characteristic curve) and its 95% CI. Statistical analyses were performed using SPSS 15.0 (SPSS) and SAS 9.1 (SAS Institute, Inc).

Results

There were 497 IDEATel participants in the telemedicine arm with complete baseline data. Of those, 47 were excluded because they had macroalbuminuria at baseline and 58 because they had <2 follow-up ACR measurements, leaving 392 participants for analysis. The mean follow-up time was 32.1±8.4 months. Of the 103 participants who had microalbuminuria at baseline, 40 exhibited improvement during follow-up; those participants were combined in all of the analyses with participants without progression to preserve the statistical power of the sample.

There were 74 participants who exhibited progression of albuminuria. As compared with participants without progression of albuminuria, those with progression were older and had a longer history of diabetes, higher home SBP and PP, higher 24-hour PP, and a more frequent nocturnal rise in SBP (Table 1).

The median (interquartile range) number of home BP measurements obtained by the participants was 51 (28 to 63), and the median time from the baseline examination to the first home BP measurement was 18 (15 to 25) days. Home SBP and PP tended to be lower than the corresponding office measurements, but the diastolic pressure was higher. The 24-hour ABP levels were the lowest. Home BP measurements were significantly correlated with office and ambulatory measurements (Table S1).

The proportion of people who exhibited progression of albuminuria in different BP categories is summarized in Table 2. When BP control was defined based on cutoff values of systolic and diastolic BP, the proportion of participants with progression of albuminuria was similar in those with controlled and uncontrolled BP. On the other hand, when a home PP of ≥60 mm Hg (the cutoff value selected through receiver operating characteristic curve analysis) was present, 24% exhibited progression of albuminuria, as compared with 14% of those with home PP <60 mm Hg (P=0.014). Similar trends were observed for office and 24-hour PP (data not shown).

The mixed linear models (Table 3) showed that, adjusting for other baseline characteristics, both office and home PP were independently associated with higher urine ACR at follow-up (P=0.018 and 0.001, respectively). When ABPM variables were introduced into the model, home PP was independently associated with ACR (P=0.011), as were 24-hour PP, and a nocturnal BP rise. Other variables independently associated with higher ACR in both mixed models were black and Hispanic ethnicity, active smoking, duration of diabetes mellitus, and a lower body mass index.

Table 3. Results of Mixed Linear Models for Progression of Albuminuria in 392 Participants Without Macroalbuminuria at Baseline

Cox proportional hazards models (Table 4) were adjusted for baseline urine ACR and those covariates that reached statistical significance in the mixed models. In model 1, which adjusted for those covariates and office PP, only home PP was an independent predictor of progression of albuminuria (P=0.013). The hazard ratio (95% CI) per 10 mm Hg of home PP was 1.36 (1.07 to 1.74). In the second model, which included as predictors home PP and ABPM measurements (but not office PP), only a nocturnal BP rise was an independent predictor (P=0.03). When office PP was removed from this model, nocturnal BP rise remained an independent predictor, whereas home PP exhibited a trend but did not reach statistical significance (P=0.06; data not shown).

Table 4. Results of Proportional Hazards Model for Progression of Albuminuria in 392 Participants Without Macroalbuminuria at Baseline

Finally, a third multivariate-adjusted Cox model was fitted. It included the ABPM variables (24-hour PP and nocturnal BP patterns) as predictors and adjusted for office PP but did not include home PP. This model was compared with model 1. Their calibration, as measured by the −2 log-likelihood statistic, was similar. The −2 log-likelihood was 779.01 for the home PP model and 775.46 for the ABPM model (P=0.169 for the likelihood ratio test). Both models also performed similarly in terms of discrimination. The c-statistic (95% CI) was 0.71 (0.64 to 0.79) for the model with home PP and 0.70 (0.62 to 0.78) for that with 24-hour PP and nocturnal BP patterns.

Discussion

Our main findings are 2-fold, as follows. First, home monitoring adds significantly to office BP measurements to predict the progression of albuminuria in elderly people with diabetes and may constitute an adequate substitute to ABPM in that regard. Second, a nocturnal BP rise, assessed by ABPM, is the strongest BP variable to predict the progression of albuminuria, as shown previously in patients with type 1 diabetes mellitus17 and as we have reported previously.19 Our earlier report analyzed data from participants in both the intervention and control arms of the IDEATel Study (n=957). We found that ABPM is superior to office BP to predict worsening albuminuria and that 24-hour PP and a nocturnal BP rise are independently associated with worsening albuminuria. In this article, we report on data collected in participants from the intervention arm (n=392), because only this arm included home BP measurements. We are now able to compare all 3 types of BP measurements, office, ABPM, and home BP. Our data suggest that both ABPM and home BP are superior to office BP to predict the progression of albuminuria. ABPM seems to be the most informative modality of BP assessment, in particular, its evaluation of nocturnal BP patterns. However, home BP measurements achieved approximately similar accuracy to ABPM in multivariate models.

Our findings emphasize the clinical importance of BP measurements performed outside of clinics and doctor’s offices. Both ABPM and home BP allow the acquisition of a larger number of measurements than the office setting, are not subject to the white coat effect, and capture BP values at different times of the day. ABPM seems to convey the most information, mostly through nocturnal BP measurements and measurements obtained during activities of daily life. Home monitoring may constitute an appealing alternative to clinicians because of its lower costs and high patient acceptability. However, further studies are needed to better assess the loss of predictive information that takes place when using home monitoring instead of ABPM.

This is an observational study nested within the intervention arm of a randomized trial, and there might be concerns that the association of BP measurements with albuminuria may have been confounded by changes in medications prompted by higher BP values. However, such confounding would have decreased the observed association of BP with worsening albuminuria, because participants with higher BP should have been targeted for more intensive antihypertensive treatment. In addition, both office and home BP data were known to participants and their primary care providers and were, thus, subject to the same potential confounding by increased antihypertensive treatment.

Several limitations are noteworthy. First, ACR was measured using a single spot urine sample, whereas a 24-hour urine collection, or 3 measurements instead of 1, would have provided a more accurate assessment of renal albumin excretion. However, assessment of albuminuria in a spot urine sample has been accepted as a valid alternative in large studies,38–41 and there is no reason to expect that misclassification of albuminuria caused by our measurement procedure would be differential with respect to the predictors. Second, our sample was composed of older subjects, with long-standing diabetes, prevalent end-organ damage, and advanced arterial stiffness at the time of the evaluation. Thus, our findings may not generalize to younger patients and particularly to those with type 1 diabetes. Third, like all observational studies, ours is subject to the risk of residual confounding because of poorly measured or unmeasured confounders. In addition, although medication use may be used as a covariate, it could not be analyzed as a predictor itself, because of the potential confounding by indication. Finally, the IDEATel baseline examination did not include an assessment of renal function, such as a serum creatinine. Patients with advanced renal failure were excluded, but we do not know whether the addition of a serum creatinine measurement would have substantially changed our results.

The strengths of this study include a longitudinal design and a large sample that was well characterized, elderly, multiethnic, and had an adequate representation of women. Home BP measurements were acquired systematically as part of a telemedicine intervention, and ambulatory BP monitoring was performed using a well-validated methodology.42

Perspectives

Our main findings are that nocturnal BP rise seems to be the strongest BP predictor of progression of albuminuria in elderly people with diabetes mellitus, and home BP monitoring adds significantly to office BP, and more modestly to ABPM, to predict worsening of albuminuria in elderly people with type 2 diabetes. Our data also suggest that home BP monitoring may constitute an adequate substitute to ABPM for that purpose. These findings are clinically relevant, because progression of albuminuria is associated with a higher risk of major cardiovascular events. In addition, home BP monitoring carries a lower cost than ABPM and is rapidly gaining acceptance among patients.

Acknowledgments

Source of Funding

This research was supported by cooperative agreement 95-C-90998 from the Centers for Medicare and Medicaid Services.